Integrating Financial Perspectives in Examining the Factors and Context of E-Commerce Utilization among Selected Canadian Firms
Bibliographic record
Abstract
Objectives: The study aims to examine the factors and context that encourage the adoption of e-commerce among selected Canadian companies. Methods: The study employed Benaroch use of real-option theory in assessing risk factors from 811 Canadian companies. Furthermore, the NEBIC model was used to analyse firms’ capacity in managing e-commerce. Data were analysed using maximum likelihood estimation, correlation matrix, and t-test of means equality. Findings: The study arrived at the following conclusion on the basis of the results obtained: technology-competent employees, competitive industry, and high variability of consumer sales positively correlate with the decision to use e-commerce. Applications: The study also found the agility of the firm to work on e-commerce positively correlates with e-commerce usage. Agility is attained by intensive e-commerce technology in-house training, encouraging its customers to use its e-commerce facility, and promoting e-commerce among other members of the industry.Keywords: E-commerce, Information Technology Investment, Real Option, Selling Online
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".